Adopting Autonomic Computing Capabilities in Existing Large-Scale Systems
Bibliographic record
Abstract
In current DevOps practice, developers are responsible for the operation and maintenance of software systems. However, the human costs for the operation and maintenance grow fast along with the increasing functionality and complexity of software systems. Autonomic computing aims to reduce or eliminate such human intervention. However, there are many existing large systems that did not consider autonomic computing capabilities in their design. Adding autonomic computing capabilities to these existing systems is particularly challenging, because of 1) the significant amount of efforts that are required for investigating and refactoring the existing code base, 2) the risk of adding additional complexity, and 3) the difficulties for allocating resources while developers are busy adding core features to the system. In this paper, we share our industrial experience of re-engineering autonomic computing capabilities to an existing large-scale software system. Our autonomic computing capabilities effectively reduce human intervention on performance configuration tuning and significantly improve system performance. In particular, we discuss the challenges that we encountered and the lessons that we learned during this re-engineering process. For example, in order to minimize the change impact to the original system, we use a variety of approaches (e.g., aspect-oriented programming) to separate the concerns of autonomic computing from the original behaviour of the system. We also share how we tested such autonomic computing capabilities under different conditions, which has never been discussed in prior work. As there are numerous large-scale software systems that still require expensive human intervention, we believe our experience provides valuable insights to software practitioners who wish to add autonomic computing capabilities to these existing large-scale software systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".